In [3]:
!pip install folium
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import HeatMap
from folium.plugins import HeatMapWithTime
import datetime
import statsmodels.formula.api as sm
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original = pd.read_excel('Traffic_big.xlsx')
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# Converting the Latitude and Longitude Attributes to a Float
original["Latitude"] = original["Latitude"].astype(float)
original["Longitude"] = original["Longitude"].astype(float)
# Combining The Datetime and TimeStamp together to form Datetime Objects
dt = []
for index, row in original.iterrows():
time = datetime.datetime.combine(row['Date Of Stop'], row['Time Of Stop'])
dt.append(time)
original['datetime'] = pd.to_datetime(dt)
original = original[original["Gender"] != "U"]
original = original[(original["Year"] != 0) & (original["Year"] < 2020) & (original["Year"] > 1900)]
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filtered_cols = ["Date Of Stop", "Time Of Stop", "datetime", "SubAgency",
"Description", "Location", "Latitude", "Longitude",
"Accident", "Belts", "Personal Injury", "Property Damage",
"Fatal", "Alcohol", "Work Zone", "VehicleType",
"Year", "Make", "Violation Type", "Contributed To Accident",
"Race", "Gender", "DL State"]
# Can break up the criteria above to make the dataframe more tidy
fil_df = original[filtered_cols]
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# Make a sample (of size 5000) of the Traffic Violation dataset
sam = fil_df.sample(n = 5000)
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def generate_map(loc = [39.1247, -77.1905], zoom = 10.5, tile = "openstreetmap"):
res_map = folium.Map(location = loc, zoom_start = zoom, control_scale = True, tiles = tile)
# Add the Tile (or Style) of the Map
folium.TileLayer('openstreetmap').add_to(res_map)
folium.TileLayer('Stamen Watercolor').add_to(res_map)
folium.TileLayer('Stamen Toner').add_to(res_map)
return res_map
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# This Function returns the designated color assigned to a race.
def color_select(race):
ethnicity = {'ASIAN': "#ed8134", # Orange
'BLACK': "#391cba", #Indigo
'HISPANIC': "#119992", #Teal
'NATIVE AMERICAN': "#9412b8", # Violet
'OTHER': "#127bb8", # Blue
'WHITE': "#e81c1c"} # Red
return ethnicity[race]
Map Exploring the Race and Gender¶
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# Creating an Empty Map
map_total = generate_map()
# other mapping code (e.g. lines, markers etc.)
asian_fg = folium.FeatureGroup(name = "Asian")
black_fg = folium.FeatureGroup(name = "Black")
his_fg = folium.FeatureGroup(name = "Hispanic")
na_fg = folium.FeatureGroup(name = "Native American")
other_fg = folium.FeatureGroup(name = "Other")
white_fg = folium.FeatureGroup(name = "White")
race = {'ASIAN': asian_fg,
'BLACK': black_fg,
'HISPANIC': his_fg,
'NATIVE AMERICAN': na_fg,
'OTHER': other_fg,
'WHITE': white_fg}
for ind, row in sam.iterrows():
entry = (folium.RegularPolygonMarker(location = [row["Latitude"],row["Longitude"]], popup = row["Description"],
color= color_select(row["Race"]), fill = True, weight = 1,
number_of_sides = 3 if row["Gender"] == "M" else 6,
radius = 4, opactity = .4))
entry.add_to(race[row["Race"]])
for r in race:
race[r].add_to(map_total)
folium.LayerControl().add_to(map_total)
map_total
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Bar Graph Relating Gender, Race, and the number of Traffic Violation¶
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gr_df = sam.copy()
gr_df["count"] = 1
aggregation_functions = {'count': 'sum'}
nd = gr_df.groupby(['Gender', 'Race']).aggregate(aggregation_functions)
# Setting up the plot and dimension
fig, axs = plt.subplots()
fig.set_figheight(30)
fig.set_figwidth(40)
b1 = sns.barplot(x="Gender", y ="count", hue="Race", palette = "Spectral", data=nd.reset_index(), ax = axs)
b1.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=3, labelspacing=2, fontsize = 20)
b1.set_title("The Occurrence of Traffic Violation Based on Gender and Race", fontsize = 40)
b1.set_ylabel("Count", fontsize = 30)
b1.set_xlabel("Gender", fontsize = 30)
b1.tick_params(axis='both', labelsize=25)
plt.show()
HeatMap Exploring the Occurrence based on Time (Hours)¶
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sam["hour"] = [t.hour for t in sam["Time Of Stop"]]
cut = pd.cut(sam["hour"], bins = [0,2,4,6,8,10,12,14,16,18,20,22,24],
labels = [1,2,3,4,5,6,7,8,9,10,11,12], right = False)
sam["cut"] = cut
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df_copy = sam.copy()
df_copy['count'] = 1
hr_map = generate_map()
hm_fg = []
hr = 0
for ind in range(12):
temp_name = "Hours " + str(hr) + " to " + str(hr + 1)
hm_fg.append(folium.FeatureGroup(name = temp_name, show= True if ind == 0 else False))
hr += 2
# Group time together to have more during a specifc set of hours\
for index in range(12):
temp = df_copy[df_copy["cut"] == index + 1]
HeatMap(data=temp[['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude', 'count'])
.sum()
.reset_index()
.values.tolist(),
radius=8, max_zoom=13).add_to(hm_fg[index])
for fg in hm_fg:
fg.add_to(hr_map)
folium.LayerControl().add_to(hr_map)
hr_map
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HeatMap Over Time¶
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time_map = generate_map()
df_hour_list = []
for hour in df_copy["cut"].sort_values().unique():
df_hour_list.append(df_copy.loc[df_copy.hour == hour, ['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude']).sum().reset_index().values.tolist())
HeatMapWithTime(df_hour_list, radius=8, gradient={0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 1: 'red'},
min_opacity=0.5, max_opacity=0.8, use_local_extrema=True, auto_play=True).add_to(time_map)
folium.LayerControl().add_to(time_map)
time_map
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Total HeatMap¶
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df_copy = sam.copy()
df_copy['count'] = 1
base_map = generate_map()
HeatMap(data=df_copy[['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude', 'count'])
.sum()
.reset_index()
.values.tolist(),
radius=8, max_zoom=13).add_to(base_map)
folium.LayerControl().add_to(base_map)
base_map
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Exploring the Vehicle Type and Year¶
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##### VehicleType, Year, Make
vy_df = sam.copy()
vy_df['count'] = 1
vy_df = vy_df.sort_values("Year")
aggregation_functions = {'count': 'sum'}
nd2 = vy_df.groupby(['Year', 'VehicleType']).aggregate(aggregation_functions)
# Setting up the plot and dimension
fig, axs = plt.subplots()
fig.set_figheight(30)
fig.set_figwidth(40)
l = sns.lineplot(x = "Year", y = "count", hue = "VehicleType",data = nd2.reset_index(), ax = axs)
l.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=10, labelspacing=2, fontsize = 18)
l.set_title("The Occurrence of Traffic Violation Based on the Vehicle Type and Year", fontsize = 40)
l.set_ylabel("Occurrence", fontsize = 30)
l.set_xlabel("Year", fontsize = 30)
l.tick_params(axis='both', labelsize=25)
plt.show()
Linear Regression¶
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district = np.unique(original["SubAgency"])
simp_district = {}
for dist in district:
if dist[0] == "H":
simp_district[dist] = 7
else:
simp_district[dist] = int(dist[0])
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race_num = {'ASIAN': 0,
'BLACK': 1,
'HISPANIC': 2,
'NATIVE AMERICAN': 3,
'OTHER': 4,
'WHITE': 5}
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vt = {"Warning": 1,
"Citation": 2,
"ESERO": 3,
"SERO": 4}
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data_reg = sam.copy()
data_reg["district_num"] = [simp_district[d] for d in data_reg["SubAgency"]]
data_reg["gender_num"] = [0 if g == "M" else 1 for g in data_reg["Gender"] ]
data_reg["race_num"] = [race_num[r] for r in data_reg["Race"]]
data_reg["violation_type_num"] = [vt[v] for v in data_reg["Violation Type"]]
data_reg
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#mlr = sm.ols(formula = 'lifeExp ~ year + continent_Asia + continent_Africa + continent_Europe + continent_Americas + continent_Oceania + year*continent_Asia + year*continent_Africa + year*continent_Europe + year*continent_Americas + year*continent_Oceania', data = data).fit()
distlr = sm.ols(formula = 'district_num ~ Year + gender_num + race_num + violation_type_num', data = data_reg).fit()
distlr.summary()
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